A CCTV system which is an image surveilling device has been applied to various fields. In the existing CCTV as the surveilling device, a person directly confirms most of photographed contents using an analog video recorder (AVR), a digital video recorder (DVR), and a network video recorder (NVR) to determine abnormal behaviors. Therefore, the existing surveilling device requires many operating personnel to directly determine an abnormal behavior and it is easy for the operating personnel to miss objects or behaviors to be identified. Therefore, there is a need to develop an intelligent surveilling device that may automatically monitor a specific object or a human behavior without a person surveiling an image for 24 hours and then notifies a user of the surveilled result and may quickly cope with abnormal behaviors.
As a method for detecting a person using 2D image information, there are a method for using an image difference between two frames, a method for creating static/dynamic background models, a method for using learning, or the like.
The method for using an image difference between two frames is a method for calculating a difference between pixel values of corresponding coordinates in a previous frame and a current frame. When a moving object is present between the two frames, the principle that the difference between the pixel values has a value other than 0 has been used.
The method for creating a background model is divided into a static background modeling method and a dynamic background modeling method. The static background modeling method is a method for accumulating a pixel value of an image without intrusion of a person/object for a predetermined time after a camera is driven to calculate an average value to thereby create a background model and obtaining a difference between the background model and a current incoming image. However, the static background modeling method generates a region in which the object/person is absorbed into the background model when the object/person is entered while the background model is created and thus is not detected.
The dynamic background modeling method updates the background model at a predetermined time interval to improve the static background modeling method. However, the dynamic background modeling method has a drawback in that when the person/object is intruded and thus stays at the same location without moving, the intruded person/object is absorbed into the background.
Meanwhile, the method for using learning is a method for manually generating data about a person's shape in advance, learning the generated data in adaptive boosting (AdaBoost), a neural network, a support vector machine (SVM), or the like, and searching whether objects similar to data learned in a current image are present. The method has a drawback in that a large amount of learning data needs to be manually collected in advance and person detection performance relies on the collected learning data.
The existing methods as described above detect a person and calculate a size, a motion, or the like of a person/object on the basis of the detected pixel information to detect abnormal behaviors. The method for using a 2D image detects a person using only color information and extracts features and therefore sensitively reacts to a change in surrounding environment like a change in illumination, covering the image with a shadow or a thing, or the like to increase an incorrect reporting rate.